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Principal component analysis along with several classification schemes were used to identify nitrogen oxides, ammonia, sulfur dioxide, and other gaseous pollutants.
To develop such signatures we have initially tested a univariate variable selection strategy based on an F test in combination with several classification methods (SVM, DLDA, PAMR, KNN, SOM) as implemented in the software application Prophet available in the Web based microarray analysis suite GEPAS [53].
The uncorrelated feature sets were used with several classification algorithms to determine which set of features produced the highest area under the curve (AUC).
The expected mortality was computed using the k-nearest neighbours' method and compared with several classification algorithms (logistic regression, random forest, support vector machine, Gaussian mixture models).
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Since there have been few studies on the relationship among such different criteria on RMR or Q-value, this study mainly focuses on the comparison of the conventional guideline for rock mass classification with several individual classification schemes in Korea.
In order to obtain more realistic models with better interpretability, several classification models have been proposed.
In our multimodal emotion recognition system, recorded signals with the formation of several classification units identified the emotions independently.
One way of conducting this experiment is to implement several classification models with different input sizes; however, this process is computationally expensive as the number of free parameters of the model is related to the input size.
Its clinical relevance must be interpreted with caution, as there are several classification systems available in the literature (Becker et al, 2003, 2011; Fujitani et al, 2012), with classification of responders varying from a complete HPR to <50% residual tumour (Becker et al, 2003; Mansour et al, 2007; Fujitani et al, 2012).
We have build several classification tree models with different misclassification costs in the algorithm.
Several classification schemes, each with its own philosophy and categorizing method, subgroup low back pain (LBP) patients with the intent to guide treatment.
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